In [1]:
import pandas as pd
import os
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
from sklearn.preprocessing import scale
import pandas as pd
import seaborn as sns
import numpy as np

Normal Sugar Level Before meal <100mg/Dl (bw 60-90 is ideal) After Meal <140mg/Dl

Ideal Blood Pressure is B/W 80-120

Tricep Normal SkinFold Thickness Female= 15-16.5mm

Insulin <100 - Normal ; between 100-125- Pre-Diabetic ; >125-Diabetic

Normal BMI - 18.5-24.9

Theoretically glucose and insulin has direct correlation

Statistic Skewness Values:

< -1 or > 1 highly skewed

between -1 and -.5 or .5 and 1 distribution is moderately skewed

between -.5 and .5 distribution is approximately symmetric

In [42]:
Diabetes=pd.read_csv("diabetes.csv")

Data Description

In [223]:
Diabetes.describe()
Out[223]:
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome
count 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000
mean 3.845052 120.894531 69.105469 20.536458 79.799479 31.992578 0.471876 33.240885 0.348958
std 3.369578 31.972618 19.355807 15.952218 115.244002 7.884160 0.331329 11.760232 0.476951
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.078000 21.000000 0.000000
25% 1.000000 99.000000 62.000000 0.000000 0.000000 27.300000 0.243750 24.000000 0.000000
50% 3.000000 117.000000 72.000000 23.000000 30.500000 32.000000 0.372500 29.000000 0.000000
75% 6.000000 140.250000 80.000000 32.000000 127.250000 36.600000 0.626250 41.000000 1.000000
max 17.000000 199.000000 122.000000 99.000000 846.000000 67.100000 2.420000 81.000000 1.000000

It is evident that the data has anomalies and data has skewness.:

1) If we see the all 4 quartiles of pregnancy , there is huge deviation between 75% and maximum, hence there lies an anomaly in the variable. Such pattern exist in other variables

2) Variables like Glucose, BP, SkinThickness, Insulin, BMI can never be zero, hence it is an anomaly , in these variable there are lower and upper limit outliers , which shall be treated as per industry standards

In [225]:
Diabetes.shape # Shape of data is R-768 C-9
Out[225]:
(768, 9)
In [227]:
Diabetes.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 768 entries, 0 to 767
Data columns (total 9 columns):
Pregnancies                 768 non-null int64
Glucose                     768 non-null int64
BloodPressure               768 non-null int64
SkinThickness               768 non-null int64
Insulin                     768 non-null int64
BMI                         768 non-null float64
DiabetesPedigreeFunction    768 non-null float64
Age                         768 non-null int64
Outcome                     768 non-null int64
dtypes: float64(2), int64(7)
memory usage: 54.1 KB
In [6]:
cols=['Glucose','BloodPressure','SkinThickness','Insulin','BMI','DiabetesPedigreeFunction']
Diabetes[~(Diabetes[cols]==0).any(axis=1)].shape
Out[6]:
(392, 9)
In [62]:
pd.pivot_table?
In [67]:
pd.crosstab?
In [106]:
x=pd.crosstab(index=Diabetes.Outcome,columns=Diabetes.Pregnancies)
In [74]:
x.to_csv("p.csv")
In [107]:
x=pd.DataFrame(x)
In [108]:
x.cumsum(axis=1)
Out[108]:
Pregnancies 0 1 2 3 4 5 6 7 8 9 10 11 12 13
Outcome
0 73 179 263 311 356 392 426 446 462 472 486 490 495 500
1 38 67 86 113 136 157 173 198 220 238 248 255 259 268
In [100]:
x.
Out[100]:
Pregnancies 0 1 2 3 4 5 6 7 8 9 10 11 12 13 csum
Outcome
0 73 106 84 48 45 36 34 20 16 10 14 4 5 5 611
1 38 29 19 27 23 21 16 25 22 18 10 7 4 9 403
In [104]:
x[x.index==1]=0
In [105]:
x
Out[105]:
Pregnancies 0 1 2 3 4 5 6 7 8 9 10 11 12 13 csum
Outcome
0 73 106 84 48 45 36 34 20 16 10 14 4 5 5 611
1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Data Exploration on Basis of Outcome

In [228]:
Diabetes.groupby(['Outcome']).agg(['mean','std'])
Out[228]:
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age
mean std mean std mean std mean std mean std mean std mean std mean std
Outcome
0 3.298000 3.017185 109.980000 26.141200 68.184000 18.063075 19.664000 14.889947 68.792000 98.865289 30.304200 7.689855 0.429734 0.299085 31.190000 11.667655
1 4.865672 3.741239 141.257463 31.939622 70.824627 21.491812 22.164179 17.679711 100.335821 138.689125 35.142537 7.262967 0.550500 0.372354 37.067164 10.968254

Glucose

1) Glucose or Sugar Level in Blood can Neevr be 0 hence it is a proven outlier or you can consider it as a missing value

2) Average of Sugar level in Non Diabetic Woman is 109.98 with standard deviation of 26.14, thus 95.8% of women who are non diabetic lies between 57.7 & 162.26, . The kde curve for the non diabetic women is almost normal

3) Average of Sugar level in Diabetic Woman is 141.26 with standard deviation of 31.93, which states 99.25% of women sugar level lies between 77.38 & 205.14 and the data is skewed

4) Skewnes of variable is .47 which means variable is approximately symmetric, but when we bifercate Glucose by Outcome , it becomes moderately positive skewed for non diabetic

5) Its very rare for non diabetic women to have glucose level below 50 and above 170 , and for diabetic women to have glucose level below 50

In [8]:
sns.boxplot(Diabetes.Glucose)
Out[8]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a1549d668>
In [43]:
l = Diabetes.columns.values
number_of_columns=9
number_of_rows = len(l)-1/number_of_columns
plt.figure(figsize=(number_of_columns,5*number_of_rows))
for i in range(0,len(l)):
    plt.subplot(number_of_rows + 1,number_of_columns,i+1)
    sns.set_style('whitegrid')
    sns.boxplot(Diabetes[l[i]],color='green',orient='v')
    plt.tight_layout()
In [27]:
l=Diabetes.iloc[:,0:8].columns.values
In [44]:
def outliertreat(x):
    Perc_01=x.quantile(.1)
    Perc_99=x.quantile(.99)
    Perc_25=x.quantile(.25)
    Perc_75=x.quantile(.75)
    IQR=Perc_75 - Perc_25
    Lower_Lim=Perc_25 - 1.5 * IQR
    Upper_Lim=Perc_75 + 1.5 * IQR
    minimum=x.min()
    maximum=x.max()
    return pd.Series([Perc_01,Perc_99,Perc_25,Perc_75,IQR,Lower_Lim,Upper_Lim,minimum,maximum],
                     index=["Perc_01","Perc_99","Perc_25","Perc_75","IQR",
                            "Lower_Lim","Upper_Lim","minimum","maximum"])
Diabetes[l].apply(lambda x: outliertreat(x)).T
Out[44]:
Perc_01 Perc_99 Perc_25 Perc_75 IQR Lower_Lim Upper_Lim minimum maximum
Pregnancies 0.000 13.00000 1.00000 6.00000 5.0000 -6.500 13.500 0.000 17.00
Glucose 85.000 196.00000 99.00000 140.25000 41.2500 37.125 202.125 0.000 199.00
BloodPressure 54.000 106.00000 62.00000 80.00000 18.0000 35.000 107.000 0.000 122.00
SkinThickness 0.000 51.33000 0.00000 32.00000 32.0000 -48.000 80.000 0.000 99.00
Insulin 0.000 519.90000 0.00000 127.25000 127.2500 -190.875 318.125 0.000 846.00
BMI 23.600 50.75900 27.30000 36.60000 9.3000 13.350 50.550 0.000 67.10
DiabetesPedigreeFunction 0.165 1.69833 0.24375 0.62625 0.3825 -0.330 1.200 0.078 2.42
Age 22.000 67.00000 24.00000 41.00000 17.0000 -1.500 66.500 21.000 81.00
Outcome 0.000 1.00000 0.00000 1.00000 1.0000 -1.500 2.500 0.000 1.00
In [47]:
Diabetes.Pregnancies.clip_upper(13,inplace=True)
Diabetes.Glucose.clip_lower(37.125,inplace=True)
Diabetes.BloodPressure.clip_lower(35,inplace=True)
Diabetes.BloodPressure.clip_upper(107,inplace=True)
Diabetes.SkinThickness.clip_upper(80,inplace=True)
Diabetes.Insulin.clip_upper(318.125,inplace=True)
Diabetes.BMI.clip_lower(13.35,inplace=True)
Diabetes.BMI.clip_upper(50.55,inplace=True)
Diabetes.DiabetesPedigreeFunction.clip_upper(1.200,inplace=True)
Diabetes.Age.clip_upper(67,inplace=True)
In [48]:
l = Diabetes.columns.values
number_of_columns=9
number_of_rows = len(l)-1/number_of_columns
plt.figure(figsize=(number_of_columns,5*number_of_rows))
for i in range(0,len(l)):
    plt.subplot(number_of_rows + 1,number_of_columns,i+1)
    sns.set_style('whitegrid')
    sns.boxplot(Diabetes[l[i]],color='green',orient='v')
    plt.tight_layout()
In [49]:
Diabetes.groupby(['Outcome']).agg(['mean','std'])
Out[49]:
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age
mean std mean std mean std mean std mean std mean std mean std mean std
Outcome
0 3.298000 3.017185 110.202750 25.347672 69.508000 13.549210 19.664000 14.889947 65.113750 83.693801 30.527500 6.849655 0.420264 0.262928 31.14200 11.498969
1 4.835821 3.664168 141.534515 30.851612 72.880597 15.115805 22.093284 17.406429 89.583489 108.036975 35.105597 6.432723 0.531022 0.311596 37.05597 10.936000
In [52]:
Diabetes.groupby(['Outcome','Pregnancies']).agg(['mean'])
Out[52]:
Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age
mean mean mean mean mean mean mean
Outcome Pregnancies
0 0 111.945205 71.123288 21.054795 72.604452 32.252055 0.441822 27.095890
1 105.305425 66.839623 23.047170 79.656840 29.616038 0.450557 25.254717
2 105.214286 64.440476 20.107143 70.967262 29.997619 0.460869 25.833333
3 109.604167 67.166667 17.520833 60.585938 29.368750 0.358354 28.770833
4 117.555556 72.288889 18.422222 69.002778 31.255556 0.385422 30.066667
5 111.666667 74.638889 17.166667 40.368056 31.470833 0.359278 39.305556
6 115.352941 69.470588 18.705882 68.797794 29.983824 0.425618 37.147059
7 121.000000 73.850000 19.350000 71.306250 30.642500 0.405500 42.500000
8 106.625000 75.312500 12.937500 14.500000 30.693750 0.509500 49.562500
9 107.000000 70.400000 22.400000 62.812500 28.840000 0.311100 44.600000
10 117.571429 75.357143 10.571429 25.071429 30.114286 0.393857 43.357143
11 113.250000 81.000000 10.000000 0.000000 37.125000 0.259000 40.750000
12 111.000000 80.200000 24.600000 31.800000 30.560000 0.301200 48.200000
13 117.200000 74.400000 22.000000 50.000000 33.280000 0.405600 46.800000
1 0 144.236842 68.657895 24.605263 80.322368 38.475000 0.559368 28.578947
1 143.793103 72.689655 29.517241 123.939655 37.639655 0.600138 35.103448
2 135.473684 70.894737 27.210526 118.598684 34.578947 0.542474 32.947368
3 148.444444 69.444444 24.629630 121.087963 32.548148 0.525222 29.481481
4 139.913043 70.043478 10.913043 51.782609 33.873913 0.508217 37.956522
5 132.958333 78.809524 17.761905 74.863095 36.669048 0.458952 38.380952
6 134.695312 72.750000 15.375000 48.757812 31.775000 0.414125 44.000000
7 148.800000 72.520000 21.040000 84.495000 34.756000 0.474120 40.000000
8 150.000000 76.681818 20.500000 120.880682 32.811364 0.488682 42.272727
9 144.944444 81.888889 20.055556 57.555556 33.300000 0.678500 43.166667
10 125.600000 70.000000 22.900000 48.400000 32.715000 0.514500 41.700000
11 134.000000 75.285714 28.428571 102.857143 39.135714 0.651286 46.714286
12 116.750000 71.500000 30.250000 211.781250 34.575000 0.623250 46.500000
13 138.111111 75.111111 21.222222 48.555556 36.866667 0.466556 42.777778
In [53]:
Diabetes[(Diabetes.Outcome==1) & (Diabetes.Pregnancies==12)]
Out[53]:
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome
215 12 151.0 70 40 271.000 41.8 0.742 38 1
254 12 92.0 62 7 258.000 27.6 0.926 44 1
375 12 140.0 82 43 318.125 39.2 0.528 58 1
510 12 84.0 72 31 0.000 29.7 0.297 46 1
In [ ]:
In [ ]:
In [55]:
sns.distplot(Diabetes.Glucose)

/Users/himanshu/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
Out[55]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a16e9fa90>
In [56]:
x=sns.FacetGrid(Diabetes,col='Outcome')
x=x.map(sns.distplot,'Glucose')

/Users/himanshu/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
In [57]:
Diabetes.Glucose.skew()
Out[57]:
0.4179462206775283
In [ ]:
In [8]:
Diabetes.Glucose[Diabetes.Outcome==1].shape
Out[8]:
(268,)
In [9]:
Perc_concentration_diabetic_women=266*100/268
In [10]:
Diabetes.Glucose[(Diabetes.Outcome==0) & (Diabetes.Glucose>57.6) & (Diabetes.Glucose <162.27)].shape
Out[10]:
(477,)
In [11]:
Diabetes.Glucose[Diabetes.Outcome==0].shape
Out[11]:
(500,)
In [13]:
Perc_concentration_non_diabetic_women=477*100/500
In [58]:
x=sns.FacetGrid(Diabetes,col='Outcome')
x=x.map(sns.boxplot,'Glucose')

/Users/himanshu/anaconda3/lib/python3.7/site-packages/seaborn/axisgrid.py:715: UserWarning: Using the boxplot function without specifying `order` is likely to produce an incorrect plot.
  warnings.warn(warning)
In [27]:
sns.boxplot(Diabetes.Glucose)
Out[27]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a162d9eb8>
In [19]:
iqr=Diabetes.Glucose.quantile(.75)-Diabetes.Glucose.quantile(.25)
Lower_ND_Cut_G=Diabetes.Glucose.quantile(.25)- 1.5 * iqr
In [18]:
Lower_ND_Cut_G
Out[18]:
37.125
In [21]:
Diabetes.Glucose.quantile(.10)
Out[21]:
85.0
In [23]:
Diabetes.Glucose.quantile(.01)
Out[23]:
57.0

Since 1% of Glucose data is 57 and below so i would consider 37.125 as the lower limit for outlier treatment

In [24]:
Diabetes.Glucose.clip_lower(Lower_ND_Cut_G,inplace=True)
In [25]:
x=sns.FacetGrid(Diabetes,col='Outcome')
x=x.map(plt.hist,'Glucose')
In [26]:
x=sns.FacetGrid(Diabetes,col='Outcome')
x=x.map(sns.boxplot,'Glucose')

/Users/himanshu/anaconda3/lib/python3.7/site-packages/seaborn/axisgrid.py:715: UserWarning: Using the boxplot function without specifying `order` is likely to produce an incorrect plot.
  warnings.warn(warning)
In [59]:
sns.swarmplot(Diabetes.Outcome,Diabetes.Glucose)
Out[59]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a167c2ac8>
In [112]:
mean_Glucose_Level_non_Diabetic=Diabetes.Glucose[Diabetes.Outcome==0].mean()
sd_Glucose_Level_non_Diabetic=Diabetes.Glucose[Diabetes.Outcome==0].std()
mean_Glucose_Level_Diabetic=Diabetes.Glucose[Diabetes.Outcome==1].mean()
sd_Glucose_Level_Diabetic=Diabetes.Glucose[Diabetes.Outcome==1].std()

Pregnancies

1) Women who are non diabetic have on an average 3 children with standard deviation of 3 , which means 94.4% of females are having number of babies between 0 to 9

2) Women who are diabetic have on an average 5 children with standard deviation of 4, which means 97% of females are having number of babies between 0 to 12

3) This study doesnt state that diabetes have huge or severe complication in delivery of child

4) Pregnancy variable has moderate positive skewness , but when we bifercate it as per outcome variable then it is highly positive skewed when outcome variable is 0 else it is approximately symmetric

5) For non diabetic women , women having pregnancy above 11 is a rare

In [29]:
sns.distplot(Diabetes.Pregnancies)

/Users/himanshu/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
Out[29]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a164f75c0>
In [30]:
x=sns.FacetGrid(Diabetes,col='Outcome')
x=x.map(plt.hist,'Pregnancies')
In [31]:
sns.boxplot(Diabetes.Pregnancies)
Out[31]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a15aaa5c0>
In [32]:
IQR_P=Diabetes.Pregnancies.quantile(.75)-Diabetes.Pregnancies.quantile(.25)
U_Cut_P=Diabetes.Pregnancies.quantile(.75)+1.5*IQR_P
Diabetes.Pregnancies.clip_upper(U_Cut_P,inplace=True)
In [33]:
Diabetes.Pregnancies.skew()
Out[33]:
0.8539617478323778
In [34]:
Diabetes.Pregnancies[Diabetes.Outcome==0].skew()
Out[34]:
1.114105089511448
In [35]:
Diabetes.Pregnancies[Diabetes.Outcome==1].skew()
Out[35]:
0.41272712073768253
In [36]:
x=sns.FacetGrid(Diabetes,col='Outcome')
x=x.map(sns.distplot,'Pregnancies')

/Users/himanshu/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
In [37]:
x=sns.FacetGrid(Diabetes,col='Outcome')
x=x.map(sns.boxplot,'Pregnancies')

/Users/himanshu/anaconda3/lib/python3.7/site-packages/seaborn/axisgrid.py:715: UserWarning: Using the boxplot function without specifying `order` is likely to produce an incorrect plot.
  warnings.warn(warning)
In [38]:
mean_Preg_non_Diabetic=Diabetes.Pregnancies[Diabetes.Outcome==0].mean()
sd_Preg_non_Diabetic=Diabetes.Pregnancies[Diabetes.Outcome==0].std()
mean_Preg_Diabetic=Diabetes.Pregnancies[Diabetes.Outcome==1].mean()
sd_Preg_Diabetic=Diabetes.Pregnancies[Diabetes.Outcome==1].std()
In [39]:
sns.swarmplot(Diabetes.Outcome,Diabetes.Pregnancies)
Out[39]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a166082b0>
In [40]:
Diabetes.Pregnancies[(Diabetes.Outcome==0)&(Diabetes.Pregnancies>=0)&(Diabetes.Pregnancies<10)].shape
Out[40]:
(472,)
In [41]:
Diabetes.Pregnancies[Diabetes.Outcome==0].shape
Out[41]:
(500,)
In [42]:
Perc_Concentration_Non_Diabetic=472/5
In [43]:
Perc_Concentration_Non_Diabetic
Out[43]:
94.4
In [44]:
Diabetes.Pregnancies[(Diabetes.Outcome==1)&(Diabetes.Pregnancies>=0)&(Diabetes.Pregnancies<13)].shape
Out[44]:
(259,)
In [45]:
Diabetes.Pregnancies[Diabetes.Outcome==1].shape
Out[45]:
(268,)
In [46]:
Perc_Concentration_Diabetic=259*100/268
In [47]:
Perc_Concentration_Diabetic
Out[47]:
96.64179104477611

Blood Pressure

1) Average Blood Pressure of Non-Diabetic Women is 69.5 with standard deviation of 13.54 , which implies 94% of women blood pressure lies between 42.4 and 96.6. Majority of women have blood pressure level in between 60 to 80

2) Average Blood Pressure of Diabetic Women is 72.9 with standard deviation of 15.11 , which implies 91% of women blood pressure lies between 42.6 and 103.1. Majority of women have BP in between 60 to 95

3) It implies that people having diabetes can have there blood pressure level in control which is between 80-120 as ideal BP level is 80-120

4) Blood Pressure Variable is approximately symmetrical, but if we bifercate it as per outcome, it is approximately symmetric for outcome = 0 , else it is highly negative skewed

5) There are very less number of non diabetic women whose BP is less than 40 and greater than 100.

6) There are very less number of diabetic women whose BP is less than 50 and greater than 100

In [48]:
sns.boxplot(Diabetes.BloodPressure)
Out[48]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a166c8eb8>
In [49]:
IQR_BP=Diabetes.BloodPressure.quantile(.75)-Diabetes.BloodPressure.quantile(.25)
Lower_Cut=Diabetes.BloodPressure.quantile(.25)-(1.5*IQR_BP)
In [50]:
Lower_Cut
Out[50]:
35.0
In [51]:
Diabetes.BloodPressure.quantile(.10)
Out[51]:
54.0
In [54]:
Diabetes.BloodPressure.quantile(.05)
Out[54]:
38.7
In [55]:
U_Cut_BP=Diabetes.BloodPressure.quantile(.75)+(1.5*IQR_BP)
In [56]:
U_Cut_BP
Out[56]:
107.0
In [57]:
Diabetes.BloodPressure.quantile(.75)
Out[57]:
80.0
In [58]:
Diabetes.BloodPressure.quantile(.90)
Out[58]:
88.0
In [59]:
Diabetes.BloodPressure.quantile(.99)
Out[59]:
106.0
In [60]:
Diabetes.BloodPressure.min()
Out[60]:
0
In [61]:
Diabetes.BloodPressure.max()
Out[61]:
122

Since 99% of Blood pressure of women is 106 and below and 1% of blood pressure is 0 , we will consider the upper and lower limit to be decided from IQR

In [62]:
Diabetes.BloodPressure.clip_lower(Lower_Cut,inplace=True)
Diabetes.BloodPressure.clip_upper(U_Cut_BP,inplace=True)
In [63]:
sns.distplot(Diabetes.BloodPressure)

/Users/himanshu/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
Out[63]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a167060f0>
In [64]:
Diabetes.BloodPressure.skew()
Out[64]:
-0.4060355281846069
In [65]:
Diabetes.BloodPressure[Diabetes.Outcome==0].skew()
Out[65]:
-0.31178850508610434
In [66]:
Diabetes.BloodPressure[Diabetes.Outcome==1].skew()
Out[66]:
-0.637913319651631
In [67]:
x=sns.FacetGrid(Diabetes,col='Outcome')
x=x.map(sns.distplot,'BloodPressure')

/Users/himanshu/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
In [68]:
x=sns.FacetGrid(Diabetes,col='Outcome')
x=x.map(plt.hist,'BloodPressure')
In [69]:
mean_BP_non_Diabetic=Diabetes.BloodPressure[Diabetes.Outcome==0].mean()
sd_BP_non_Diabetic=Diabetes.BloodPressure[Diabetes.Outcome==0].std()
mean_BP_Diabetic=Diabetes.BloodPressure[Diabetes.Outcome==1].mean()
sd_BP_Diabetic=Diabetes.BloodPressure[Diabetes.Outcome==1].std()
In [70]:
Diabetes.BloodPressure[(Diabetes.Outcome==0)&(Diabetes.BloodPressure>42.39)&(Diabetes.BloodPressure<96.7)].shape
Out[70]:
(470,)
In [71]:
Diabetes.BloodPressure[(Diabetes.Outcome==0)].shape
Out[71]:
(500,)
In [72]:
Perc_Conentration_Non_Diabetic=470/5
In [73]:
Perc_Conentration_Non_Diabetic
Out[73]:
94.0
In [74]:
Diabetes.BloodPressure[(Diabetes.Outcome==1)&(Diabetes.BloodPressure>42.59)&(Diabetes.BloodPressure<103.2)].shape
Out[74]:
(243,)
In [75]:
Diabetes.BloodPressure[(Diabetes.Outcome==1)].shape
Out[75]:
(268,)
In [76]:
Perc_Conentration_Diabetic=243*100/268
In [77]:
Perc_Conentration_Diabetic
Out[77]:
90.67164179104478
In [78]:
x=sns.FacetGrid(Diabetes,col='Outcome')
x=x.map(sns.boxplot,'BloodPressure')

/Users/himanshu/anaconda3/lib/python3.7/site-packages/seaborn/axisgrid.py:715: UserWarning: Using the boxplot function without specifying `order` is likely to produce an incorrect plot.
  warnings.warn(warning)
In [79]:
sns.swarmplot(Diabetes.Outcome,Diabetes.BloodPressure)
Out[79]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a16da8470>

SkinThickness

1) Mean skinfold thickness value of Non-Diabetic woman is 21.9 with standard deviation of 11.9, which states 66.8% of women have skinfold thickness of triceps between 9.3 and 42.6

2) Mean skinfold thickness value of Diabetic woman is 29.3 with standard deviation of 8.22, which states 60%% of women have skinfold thickness of triceps between 12.9 and 45.8

3) Hence SkinFold Thickness of Tricep is a good but not reliable measure of classifying in diabetic and non diabetic

4) Skinfold Thickness of a person can be never 0 hence we can consider it as missing value because data has significant amount of 0 so we have done imputation using median since it wasnt a normal distributed data

5) Skinfold Thickness variable after imputation and outlier treatment is approximately symmetric but when we bifercate it with respect to outcome variable , for non diabetic women it is moderately positive skewed, where as for diabetic women it is approximately symmetric

6) There are very less non diabetic women who have thickness above 41

7) It is a moderate skewed variable

In [ ]:
In [80]:
sns.boxplot(Diabetes.SkinThickness)
Out[80]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a16bfb518>
In [81]:
IQR_SKFT=Diabetes.SkinThickness.quantile(.75)-Diabetes.SkinThickness.quantile(.25)
ucut=Diabetes.SkinThickness.quantile(.75)+1.5*IQR_SKFT
lcut=Diabetes.SkinThickness.quantile(.25)-1.5*IQR_SKFT
In [82]:
ucut
Out[82]:
80.0
In [83]:
lcut
Out[83]:
-48.0
In [84]:
Diabetes.SkinThickness.quantile(.99)
Out[84]:
51.33000000000004
In [96]:
Diabetes.SkinThickness.quantile(.30)
Out[96]:
8.199999999999989
In [95]:
Diabetes.SkinThickness.max()
Out[95]:
99

Since 99% of women having skinthickness of 51.33 and below and there are 30% of women are having 8.2 and below , its peculiar that 29% of women have value of skinthickness as 0

so here we will use upper(99%) and lower(30%)[ since value of skinthickness can not be 0] percentiles to deal with outlier

In [ ]:
In [97]:
Diabetes.SkinThickness.clip_upper(Diabetes.SkinThickness.quantile(.99),inplace=True)
Diabetes.SkinThickness.clip_lower(Diabetes.SkinThickness.quantile(.30),inplace=True)
In [98]:
sns.distplot(Diabetes.SkinThickness)

/Users/himanshu/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
Out[98]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a16a3ea20>
In [99]:
Diabetes.SkinThickness.skew()
Out[99]:
0.29117788587424326
In [100]:
Diabetes.SkinThickness[Diabetes.Outcome==0].skew()
Out[100]:
0.41620508912691395
In [101]:
Diabetes.SkinThickness[Diabetes.Outcome==1].skew()
Out[101]:
0.04997998930333311
In [102]:
x=sns.FacetGrid(Diabetes,col='Outcome')
x=x.map(sns.distplot,'SkinThickness')

/Users/himanshu/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
In [103]:
mean_SKFT_Level_non_Diabetic=Diabetes.SkinThickness[Diabetes.Outcome==0].mean()
sd_SKFT_Level_non_Diabetic=Diabetes.SkinThickness[Diabetes.Outcome==0].std()
mean_SKFT_Level_Diabetic=Diabetes.SkinThickness[Diabetes.Outcome==1].mean()
sd_SKFT_Level_Diabetic=Diabetes.SkinThickness[Diabetes.Outcome==1].std()
In [114]:
mean_SKFT_Level_non_Diabetic + 2* sd_SKFT_Level_non_Diabetic
Out[114]:
45.859454142123724
In [ ]:
In [115]:
Diabetes.SkinThickness[(Diabetes.Outcome==0)&(Diabetes.SkinThickness>0)&(Diabetes.SkinThickness<45.9)].shape
Out[115]:
(485,)
In [116]:
Perc_Concentration_Non_Diabetic=485/5
In [117]:
Perc_Concentration_Non_Diabetic
Out[117]:
97.0
In [108]:
Diabetes.SkinThickness[(Diabetes.Outcome==1)&(Diabetes.SkinThickness>12.9)&(Diabetes.SkinThickness<45.81)].shape
Out[108]:
(162,)
In [109]:
Perc_Concentration_Diabetic=162*100/268
In [110]:
Perc_Concentration_Diabetic
Out[110]:
60.44776119402985
In [464]:
x=sns.FacetGrid(Diabetes,col='Outcome')
x=x.map(sns.boxplot,'SkinThickness')

/Users/himanshu/anaconda3/lib/python3.7/site-packages/seaborn/axisgrid.py:715: UserWarning: Using the boxplot function without specifying `order` is likely to produce an incorrect plot.
  warnings.warn(warning)
In [454]:
sns.swarmplot(Diabetes.Outcome,Diabetes.SkinThickness)
Out[454]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c18bc14a8>
In [ ]:

Insulin

1) Mean insulin level of non diabetic women is 77.9 and standard deviation is 68.4 , which means 93.2% of women insuline level lies between 0 to 214.6

2) Mean insulin level of diabetic women is 101.5 with standard deviation of 87.3, which states 100% of women have insulin level between 0 to 276.1

3) Type 1 Diabetic patients have low insulin levels but low insulin levels can be associated with other dieseases also like Hypopituitarism, pancreatic cancer or pancreatic disease. High insulin levels are generally seen in Type 2 Diabetes , but patients who are not diabetic can have high insulin level because of other health issues like hyperinsulinemia , obessity , heart disease, use of drugs, etc, so non diabetic women have high insulin because of other underlying factors

4) Insulin is highly positive skewed data , but when we bifercate it with respect to outcome variable , it is highly positive skewed for non diabetic women and moderately positive skewed data for diabetic

5) It is very rare for non diabetic women having insulin levels above 200

In [468]:
sns.boxplot(Diabetes.Insulin)
Out[468]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c18bc1fd0>
In [470]:
IQR_I=Diabetes.Insulin.quantile(.75)-Diabetes.Insulin.quantile(.25)
ucut=Diabetes.Insulin.quantile(.75) + 1.5 * IQR_I
Diabetes.Insulin.clip_upper(ucut,inplace=True)
In [471]:
sns.distplot(Diabetes.Insulin)

/Users/himanshu/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
Out[471]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c1881f748>
In [472]:
Diabetes.Insulin.skew()
Out[472]:
1.2399972461039153
In [473]:
Diabetes.Insulin[Diabetes.Outcome==0].skew()
Out[473]:
1.5338986383919997
In [474]:
Diabetes.Insulin[Diabetes.Outcome==1].skew()
Out[474]:
0.7984522834211514
In [475]:
x=sns.FacetGrid(Diabetes,col='Outcome')
x=x.map(sns.distplot,'Insulin')

/Users/himanshu/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
In [476]:
mean_insulin_Level_non_Diabetic=Diabetes.Insulin[Diabetes.Outcome==0].mean()
std_insulin_Level_non_Diabetic=Diabetes.Insulin[Diabetes.Outcome==0].std()
mean_insulin_Level_Diabetic=Diabetes.Insulin[Diabetes.Outcome==1].mean()
std_insulin_Level_Diabetic=Diabetes.Insulin[Diabetes.Outcome==1].std()
In [483]:
Diabetes.Insulin[(Diabetes.Outcome==0)&(Diabetes.Insulin>0)&(Diabetes.Insulin<214.61)].shape
Out[483]:
(466,)
In [484]:
Perc_Concentration_Non_Diabetic=466/5
In [486]:
Diabetes.Insulin[(Diabetes.Outcome==1)&(Diabetes.Insulin>0)&(Diabetes.Insulin<276.2)].shape
Out[486]:
(268,)
In [487]:
x=sns.FacetGrid(Diabetes,col='Outcome')
x=x.map(sns.boxplot,'Insulin')

/Users/himanshu/anaconda3/lib/python3.7/site-packages/seaborn/axisgrid.py:715: UserWarning: Using the boxplot function without specifying `order` is likely to produce an incorrect plot.
  warnings.warn(warning)
In [488]:
sns.swarmplot(Diabetes.Outcome,Diabetes.Insulin)
Out[488]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c1c69ae80>

BMI

1) Mean BMI level of non diabetic women is 30.5 and standard deviation is 6.8 , which means 95% of women BMI lies between 16.8 to 44.2

2) Mean BMI of diabetic women is 35.1 with standard deviation of 6.4, which states 94.4% of women have BMI between 22.2 to 47.9

3) Diabetic women are majorly concentrated on high BMI level

4) BMI variable is approximately symmetric but when we bifercate with respect to outcome, Non Diabetic women BMI is approximately symmetric whereas for Diabetic women it is the same

5) It is very rare that BMI of non diabetic women is greater than 49 and BMI of Diabetic women less than 23

In [491]:
sns.boxplot(Diabetes.BMI)
Out[491]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c187e3908>
In [490]:
IQR_BMI=Diabetes.BMI.quantile(.75)-Diabetes.BMI.quantile(.25)
lcut=Diabetes.BMI.quantile(.25)-1.5*IQR_BMI
ucut=Diabetes.BMI.quantile(.75)+1.5*IQR_BMI
Diabetes.BMI.clip_lower(lcut,inplace=True)
Diabetes.BMI.clip_upper(ucut,inplace=True)
In [492]:
sns.distplot(Diabetes.BMI)

/Users/himanshu/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
Out[492]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c19ec0550>
In [493]:
Diabetes.BMI.skew()
Out[493]:
0.13580859812143145
In [494]:
Diabetes.BMI[Diabetes.Outcome==0].skew()
Out[494]:
0.1671838433883163
In [495]:
Diabetes.BMI[Diabetes.Outcome==1].skew()
Out[495]:
0.25592996722249334
In [496]:
x=sns.FacetGrid(Diabetes,col='Outcome')
x=x.map(sns.distplot,'BMI')

/Users/himanshu/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
In [497]:
mean_BMI_Level_non_Diabetic=Diabetes.BMI[Diabetes.Outcome==0].mean()
sd_BMI_Level_non_Diabetic=Diabetes.BMI[Diabetes.Outcome==0].std()
mean_BMI_Level_Diabetic=Diabetes.BMI[Diabetes.Outcome==1].mean()
sd_BMI_Level_Diabetic=Diabetes.BMI[Diabetes.Outcome==1].std()
In [506]:
Diabetes.BMI[(Diabetes.Outcome==0)&(Diabetes.BMI>16.79)&(Diabetes.BMI<44.21)].shape
Out[506]:
(475,)
In [508]:
Perc_concentration_non_diabetic=475*100/500
In [509]:
Diabetes.BMI[(Diabetes.Outcome==1)&(Diabetes.BMI>22.19)&(Diabetes.BMI<47.9)].shape
Out[509]:
(253,)
In [510]:
Perc_concentration_non_diabetic=253*100/268
In [512]:
x=sns.FacetGrid(Diabetes,col='Outcome')
x=x.map(sns.boxplot,'BMI')

/Users/himanshu/anaconda3/lib/python3.7/site-packages/seaborn/axisgrid.py:715: UserWarning: Using the boxplot function without specifying `order` is likely to produce an incorrect plot.
  warnings.warn(warning)
In [537]:
sns.swarmplot(Diabetes.Outcome,Diabetes.BMI)
Out[537]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c1a93a550>

Diabetes Pedigree Function

1) Mean DPF level of non diabetic women is .42 and standard deviation is .26 , which means 94.8% of women DPF level lies between 0 to .95

2) Mean DPF level of diabetic women is .53 with standard deviation of .31, which states 92.5% of women have insulin level between 0 to 1.15

3) It is very rare for non diabetic women to have DPF more than 1

4) Variable is highly positive skewed , but when we bifercate it with respect to outcome, there is high positive skewness for non diabetic women and for diabetic women there is moderate positive skewness

In [516]:
sns.boxplot(Diabetes.DiabetesPedigreeFunction)
Out[516]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c16977da0>
In [515]:
IQR_DPF=Diabetes.DiabetesPedigreeFunction.quantile(.75)-Diabetes.DiabetesPedigreeFunction.quantile(.25)
ucut=Diabetes.DiabetesPedigreeFunction.quantile(.75)+1.5*IQR_DPF
Diabetes.DiabetesPedigreeFunction.clip_upper(ucut,inplace=True)
In [517]:
sns.distplot(Diabetes.DiabetesPedigreeFunction)

/Users/himanshu/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
Out[517]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c18514d68>
In [518]:
Diabetes.DiabetesPedigreeFunction.skew()
Out[518]:
1.0244278033317116
In [519]:
Diabetes.DiabetesPedigreeFunction[Diabetes.Outcome==0].skew()
Out[519]:
1.1732603512020994
In [520]:
Diabetes.DiabetesPedigreeFunction[Diabetes.Outcome==1].skew()
Out[520]:
0.7455204948307618
In [521]:
x=sns.FacetGrid(Diabetes,col='Outcome')
x=x.map(sns.distplot,'DiabetesPedigreeFunction')

/Users/himanshu/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
In [522]:
mean_DPF_Level_non_Diabetic=Diabetes.DiabetesPedigreeFunction[Diabetes.Outcome==0].mean()
sd_DPF_Level_non_Diabetic=Diabetes.DiabetesPedigreeFunction[Diabetes.Outcome==0].std()
mean_DPF_Level_Diabetic=Diabetes.DiabetesPedigreeFunction[Diabetes.Outcome==1].mean()
sd_DPF_Level_Diabetic=Diabetes.DiabetesPedigreeFunction[Diabetes.Outcome==1].std()
In [531]:
Diabetes.DiabetesPedigreeFunction[(Diabetes.Outcome==0)&(Diabetes.DiabetesPedigreeFunction>0)&(Diabetes.DiabetesPedigreeFunction<.96)].shape
Out[531]:
(474,)
In [532]:
perc_concentration_non_diabetic=474*100/500
In [534]:
Diabetes.DiabetesPedigreeFunction[(Diabetes.Outcome==1)&(Diabetes.DiabetesPedigreeFunction>0)&(Diabetes.DiabetesPedigreeFunction<1.16)].shape
Out[534]:
(248,)
In [535]:
perc_concentration_diabetic=248*100/268
In [538]:
x=sns.FacetGrid(Diabetes,col='Outcome')
x=x.map(sns.boxplot,'DiabetesPedigreeFunction')

/Users/himanshu/anaconda3/lib/python3.7/site-packages/seaborn/axisgrid.py:715: UserWarning: Using the boxplot function without specifying `order` is likely to produce an incorrect plot.
  warnings.warn(warning)
In [539]:
sns.swarmplot(Diabetes.Outcome,Diabetes.DiabetesPedigreeFunction)
Out[539]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c1abb5e10>

Age

1) Mean age of non-diabetic women is 31 and standard deviation is 11.4 , hence 92.8% of non diabetic women are between age of 8 to 54 years

2) Mean age of diabetic women is 37 and standard deviation is 10.9 , hence 96.6% of non diabetic women are between age of 15 to 59 years

3) Probablity of women between age of 20 to 30 will be non diabetic is .78 that means only 22% of women in this age range develops Diabetes

4) Probability or chances of having diabetes increases after age of 30

5) Age variable is highly positive skewed but when we bifercate it as per outcome variable , it remains same for non diabetic women , but it becomes moderate positive skewed for diabetic women, that means women having diabetes are more on the upper age side

6) There is a rare chance for women above age of 58 to be non diabetic

In [542]:
sns.boxplot(Diabetes.Age)
Out[542]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c1accb080>
In [541]:
IQR_A=Diabetes.Age.quantile(.75)-Diabetes.Age.quantile(.25)
ucut=Diabetes.Age.quantile(.75)+1.5*IQR_A
Diabetes.Age.clip_upper(ucut,inplace=True)
In [543]:
sns.distplot(Diabetes.Age)

/Users/himanshu/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
Out[543]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c1adb3748>
In [544]:
Diabetes.Age.skew()
Out[544]:
1.0671703233262797
In [545]:
Diabetes.Age[Diabetes.Outcome==0].skew()
Out[545]:
1.4883041453547952
In [546]:
Diabetes.Age[Diabetes.Outcome==1].skew()
Out[546]:
0.5591832679459625
In [547]:
x=sns.FacetGrid(Diabetes,col='Outcome')
x=x.map(sns.distplot,'Age')

/Users/himanshu/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
In [548]:
mean_age_non_diabetic=Diabetes.Age[Diabetes.Outcome==0].mean()
sd_age_non_diabetic=Diabetes.Age[Diabetes.Outcome==0].std()
mean_age_diabetic=Diabetes.Age[Diabetes.Outcome==1].mean()
sd_age_diabetic=Diabetes.Age[Diabetes.Outcome==1].std()
In [563]:
Diabetes.Age[(Diabetes.Outcome==0)&(Diabetes.Age>7)&(Diabetes.Age<55)].shape
Out[563]:
(464,)
In [564]:
Diabetes.Age[(Diabetes.Outcome==0)&(Diabetes.Age>19)&(Diabetes.Age<31)].shape
Out[564]:
(327,)
In [566]:
Diabetes.Age[(Diabetes.Outcome==1)&(Diabetes.Age>19)&(Diabetes.Age<31)].shape
Out[566]:
(90,)
In [567]:
Prob_non_Diabetic=327/(327+90)
Out[567]:
0.7841726618705036
In [559]:
Diabetes.Age[(Diabetes.Outcome==1)&(Diabetes.Age>14)&(Diabetes.Age<60)].shape
Out[559]:
(259,)
In [561]:
x=sns.FacetGrid(Diabetes,col='Outcome')
x=x.map(sns.boxplot,'Age')

/Users/himanshu/anaconda3/lib/python3.7/site-packages/seaborn/axisgrid.py:715: UserWarning: Using the boxplot function without specifying `order` is likely to produce an incorrect plot.
  warnings.warn(warning)
In [562]:
sns.swarmplot(Diabetes.Outcome,Diabetes.Age)
Out[562]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c16a358d0>

Relationship Between Variables

Pregnancies & Glucose

In [244]:
plt.scatter(Diabetes.Pregnancies,Diabetes.Glucose)
Out[244]:
<matplotlib.collections.PathCollection at 0x1c19f5ec50>

Most of the women have glucose levels in between 57 to 200 , but as the number of pregnancies increases number of women in the bracket decreases or you can say there are less number of women having pregnancies above 8

In [580]:
lm=sns.lmplot(x='Pregnancies',y='Glucose',data=Diabetes,x_jitter=True)

/Users/himanshu/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
In [579]:
lm=sns.lmplot(x='Pregnancies',y='Glucose',data=Diabetes,col='Outcome',x_jitter=True)

/Users/himanshu/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval

Glucose Level and No. Of Pregnancies doesnt have perfect linear relationship as they have high errors , hence linear regression line is not the apt best fit line , the relation seems to be more of polynomial relationship

In [587]:
sns.scatterplot(Diabetes.Pregnancies,Diabetes.Glucose,hue=Diabetes.Outcome, x_jitter=True, size=Diabetes.Age)
Out[587]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c20f07b38>

women whose pregnacy is 7 have more number of diabetic women as compared to others and between the suger level of 99 to 200, most of them are between age of 40 to 60. Most Pregnant Diabetic Women have Glucose level high above120

Pregnancies Vs Blood Pressure

In [588]:
plt.scatter(Diabetes.Pregnancies,Diabetes.BloodPressure)
Out[588]:
<matplotlib.collections.PathCollection at 0x1c20c25978>
In [597]:
sns.lmplot(x="Pregnancies",y="BloodPressure",data=Diabetes)

/Users/himanshu/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
Out[597]:
<seaborn.axisgrid.FacetGrid at 0x1c20d442e8>
In [604]:
sns.scatterplot(Diabetes.Pregnancies,Diabetes.BloodPressure,style=Diabetes.Outcome,hue=Diabetes.Insulin,size=Diabetes.Age)
Out[604]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c218f5828>
In [606]:
sns.scatterplot(Diabetes.Pregnancies,Diabetes.BloodPressure,style=Diabetes.Outcome,hue=Diabetes.Insulin,size=Diabetes.BMI)
Out[606]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c21b6a668>
In [616]:
sns.scatterplot(Diabetes.Pregnancies,Diabetes.BloodPressure,style=Diabetes.Outcome,hue=Diabetes.SkinThickness,size=Diabetes.Insulin,x_jitter=True,y_jitter=True)
Out[616]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c22360a20>

Majority of women are having blood pressure between 60 to 90 , There is no linear relationship found between Blood Pressure and Pregnancies, also the lmplot shows that Linear Regression line is not the best fit as the errors are huge, it has some polynomial sort of relation. Those who are classified as Diabetic , most of them have insulin level above 100 and are above . It can be said that those who are diabetic mostly have BMI in range of 15-45 . Blood pressure can be maintained while having diabeties . Those who have extreme level of low blood pressure i.e in the range of 40 to 55 are more on lower or no pregnancy side that is between 0 to 3 . This indicates as the number of pregnancy increases either the complication due to low blood pressure increases so they have to due dligently keep their BP in maintainable range .SkinThickness are high of both diabetic and non diabetic and even for women with pregnancy 0 , it states :

1) Women can be obessed 2) Women can be Pregnant 3) Women can have other underlying disease 4) Can be Diabetic 5) Genitically Inherited

It is very peculiar to see that even at the age around 20's women have got upto 14 pregnancies , it states that the tribe comes from some backward community where they are not educated.

Pregnancies Vs SkinThickness

In [617]:
plt.scatter(Diabetes.Pregnancies,Diabetes.SkinThickness)
Out[617]:
<matplotlib.collections.PathCollection at 0x1c2252be80>
In [621]:
sns.scatterplot(Diabetes.Pregnancies,Diabetes.SkinThickness,hue=Diabetes.Outcome)
Out[621]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c22743668>
In [622]:
sns.scatterplot(Diabetes.Pregnancies,Diabetes.SkinThickness,hue=Diabetes.Outcome,size=Diabetes.Glucose)
Out[622]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c2276eba8>
In [623]:
sns.scatterplot(Diabetes.Pregnancies,Diabetes.SkinThickness,hue=Diabetes.Outcome,size=Diabetes.Age)
Out[623]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c22990d68>

Most of the Diabetic women have higher skin thickness and the number of diabetic women having greater skin thickness increases as the number of pregnancies increases. Also non diabetic women are also there who have high level of skinthickness . It doesnt significantly say that Skinthickness is on higher side because of Diabeties , it can be one of the following reason :

1) Diabeties

2) Obesity

3) Any other disease

Those who have high skinthickness , most of the women have high sugar level, out of which most of them are diabetic

Keeping Outcome Variable asside , the graph suggest as the number of pregnancies increases , women tends to be on higher skinthickness level , it suggest that till the 4 pregnancies women can have skin thickness between 10-20 and there are significant percent of women who falls in that range , but after fourth pregnancies women tend to have skinthickness on higher level.

It's very peculiar that till the fourth pregnancy women with higher skinthickness are of age nearing to 20 , very few of 40 and above, but it comes to fifth and onwards number of pregnancies , there are most women who are 40 and above.

In [ ]:

Pregnancies Vs Insulin

In [626]:
sns.scatterplot(Diabetes.Pregnancies,Diabetes.Insulin)
Out[626]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c22d42978>
In [628]:
sns.scatterplot(Diabetes.Pregnancies,Diabetes.Insulin,hue=Diabetes.Outcome,x_jitter=True)
Out[628]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c22ed4080>
In [631]:
sns.scatterplot(Diabetes.Pregnancies,Diabetes.Insulin,hue=Diabetes.Outcome,size=Diabetes.BMI,x_jitter=True,y_jitter=True,alpha=.8)
Out[631]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c23111d68>
In [632]:
sns.scatterplot(Diabetes.Pregnancies,Diabetes.Insulin,hue=Diabetes.Outcome,size=Diabetes.Age,x_jitter=True,y_jitter=True,alpha=.8)
Out[632]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c23242978>
In [634]:
sns.scatterplot(Diabetes.Pregnancies,Diabetes.Insulin,hue=Diabetes.Outcome,size=Diabetes.BloodPressure,x_jitter=True,y_jitter=True,alpha=.8)
Out[634]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c23545cc0>
In [635]:
sns.scatterplot(Diabetes.Pregnancies,Diabetes.Insulin,hue=Diabetes.Outcome,size=Diabetes.Glucose,x_jitter=True,y_jitter=True,alpha=.8)
Out[635]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c23678198>

1) As number of pregnancies increases , number of women having insulin level above 100 decreases

2) Majority of Diabetic women are above insulin level 100

3) Women having high insulin value and being diabetic still they are pregnant , it means insulin value and being diabetic isnt much of a complication

4) Most women are having high BMI , i.e they dont fall in idle range of BMI , most diabetic women who have high BMI are having high insulin level that is 100 and above

5) Since we know that number of pregnancies by a women are not usual , they are having pregnancies till 14 , so we can not state the High BMI of women is only because of diabeties, it can be because of the following : a) Number of pregnancies b) Obessity c) Diabetes

6) Mostly of Diabetic women have high insulin with low blood pressure that is 75 and below. There are few whose blood pressure is above 75 and below 120 , there blood pressure is in idle range but they have high insulin level , i.e 150 and above

7) Mostly those women who are diabetic and have high insulin level that is above 100 have high glucose level also i.e 100 and above

In [ ]:
In [636]:
sns.scatterplot(Diabetes.Pregnancies,Diabetes.Age)
Out[636]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c23891128>
In [637]:
sns.scatterplot(Diabetes.Pregnancies,Diabetes.Age,hue=Diabetes.Outcome)
Out[637]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c2381c470>
In [638]:
sns.scatterplot(Diabetes.Pregnancies,Diabetes.Age,hue=Diabetes.Outcome,size=Diabetes.Glucose)
Out[638]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c23968a90>
In [639]:
sns.scatterplot(Diabetes.Pregnancies,Diabetes.Age,hue=Diabetes.Outcome,size=Diabetes.BloodPressure)
Out[639]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c20d449b0>
In [641]:
sns.scatterplot(Diabetes.Pregnancies,Diabetes.Age,hue=Diabetes.Outcome,size=Diabetes.BMI)
Out[641]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c23a2ec50>

1) As the age increases number of pregnancies by women decreases

2) It is peculiar to see that those women who have pregnancies above 10 they lie in age range of 35-55, which states that they would have gotten pregnant at very early stage of their life

3) Most Diabetic women are above age of 35 also have pregnancies 6 and above

4) Women above age of 30 have high glucose level that is 100 and above, low blood pressure that is 75 and below and most of them are diabetic

5) Most women age above 30 and having number of pregnancies 6 and above are having BMI on higher side that is 30 and above

Glucose vs Blood Pressure

In [642]:
sns.scatterplot(Diabetes.Glucose,Diabetes.BloodPressure)
Out[642]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c20f75710>
In [643]:
sns.scatterplot(Diabetes.Glucose,Diabetes.BloodPressure,hue=Diabetes.Outcome)
Out[643]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c23a66b38>
In [644]:
sns.scatterplot(Diabetes.Glucose,Diabetes.BloodPressure,hue=Diabetes.Outcome,size=Diabetes.Insulin)
Out[644]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c23e1e6a0>
In [646]:
sns.scatterplot(Diabetes.Glucose,Diabetes.BloodPressure,hue=Diabetes.Outcome,size=Diabetes.BMI,x_jitter=True)
Out[646]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c240f6630>
In [648]:
sns.scatterplot(Diabetes.Glucose,Diabetes.BloodPressure,hue=Diabetes.Outcome,size=Diabetes.Age,x_jitter=True)
Out[648]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c242d1438>

1) There is higher concentration of women having BP in range of 50 to 90 and Glucose in range of 90 to 150

2) Concentration of Diabetic women is more where women have BP between 50 to 90 and Glucose in range of 120-400 , the same area of concentration of diabetic women have majority of women whose insulin level is 100 and above, it states that Glucose , BP and Insulin have some relation , specifically glucose and insulin, as we can see Higher the glucose level higher the insulin

3) Most of women who are diabetic and have higher glucose level that is 120 and above have higher BMI that is 30 and above , so BMI and Glucose tend to have a relation

Glucose and SkinThickness

In [653]:
sns.scatterplot(Diabetes.Glucose,Diabetes.SkinThickness)
Out[653]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c2488fac8>
In [654]:
sns.scatterplot(Diabetes.Glucose,Diabetes.SkinThickness,hue=Diabetes.Outcome)
Out[654]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c24a46fd0>
In [655]:
sns.scatterplot(Diabetes.Glucose,Diabetes.SkinThickness,hue=Diabetes.Outcome,size=Diabetes.Pregnancies)
Out[655]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c249ecac8>
In [656]:
sns.scatterplot(Diabetes.Glucose,Diabetes.SkinThickness,hue=Diabetes.Outcome,size=Diabetes.BMI)
Out[656]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c24c15860>
In [657]:
sns.scatterplot(Diabetes.Glucose,Diabetes.SkinThickness,hue=Diabetes.Outcome,size=Diabetes.Age)
Out[657]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c24c45710>

1) As Glucose level increases , number of women having thicker skinthickness increases

2) Majority of concentration of diabetic women falls above glucose level of 120 and skin thickness 20 and above

3) Majority of diabetic women having pregnancies 5 and above had skin thickness 22.5 and above

4) Mojority of diabetic women having abnormal BMI that is 30 and above had skin thickness 22.5 and above and had glucose level 120 and above

5) Probability of women to have diabeties are very less if their skinthickness is less than 22.5

Glucose and Insulin

In [658]:
sns.scatterplot(Diabetes.Glucose,Diabetes.Insulin)
Out[658]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c24da0908>
In [659]:
sns.scatterplot(Diabetes.Glucose,Diabetes.Insulin,hue=Diabetes.Outcome)
Out[659]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c24ea00b8>
In [661]:
sns.scatterplot(Diabetes.Glucose,Diabetes.Insulin,hue=Diabetes.Outcome,size=Diabetes.BMI)
Out[661]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c25095780>
In [662]:
sns.scatterplot(Diabetes.Glucose,Diabetes.Insulin,hue=Diabetes.Outcome,size=Diabetes.BloodPressure)
Out[662]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c24da0e48>
In [663]:
sns.scatterplot(Diabetes.Glucose,Diabetes.Insulin,hue=Diabetes.Outcome,size=Diabetes.Age)
Out[663]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c24ea0b70>

1) Glucose and Insulin shows a linear relationship, i.e with increase in glucose , insulin increases

2) Major concentration of diabetic women are having glucose levels 120 and above , insulin 100 and above

3) There is no definite increase in BMI due to fact of insulin and glucose

4) Majority of diabetic women have blood pressure in control hence , insulin and glucose doesnt have any effect on blood pressure

5) Majority of diabetic women of age 40 and above have Glucose level 120 and above and insulin 100 and above

Glucose & DPF

In [664]:
sns.scatterplot(Diabetes.Glucose,Diabetes.DiabetesPedigreeFunction)
Out[664]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c24fe14a8>
In [665]:
sns.scatterplot(Diabetes.Glucose,Diabetes.DiabetesPedigreeFunction,hue=Diabetes.Outcome)
Out[665]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c254a55f8>
In [667]:
sns.scatterplot(Diabetes.Glucose,Diabetes.DiabetesPedigreeFunction,hue=Diabetes.Outcome,size=Diabetes.Insulin)
Out[667]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c256a5a20>
In [668]:
sns.scatterplot(Diabetes.Glucose,Diabetes.DiabetesPedigreeFunction,hue=Diabetes.Outcome,size=Diabetes.Age)
Out[668]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c257c2550>

1) Major concentration of women is in section where glucose level range from 80 to 130 and DPF from 0 to .8

2) Majority of Diabetic women lies in section where glucose level is 120 and above and DPF range from 0 to .8

3) The probability of diabetic women to have high insulin level when DPF is .8 and above and Glucose level is 120 and above is more as compared to women having DPF lesser than .8

4) Majority of Diabetic women age 40 and abover are concentrated in section of DPF less than .7 and glucose level 140 and above

Glucose & Age

In [671]:
sns.scatterplot(Diabetes.Glucose,Diabetes.Age)
Out[671]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c25b32550>
In [672]:
sns.scatterplot(Diabetes.Glucose,Diabetes.Age,hue=Diabetes.Outcome)
Out[672]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c25c44438>
In [674]:
sns.scatterplot(Diabetes.Glucose,Diabetes.Age,hue=Diabetes.Outcome,size=Diabetes.Insulin)
Out[674]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c259eb160>
In [677]:
sns.scatterplot(Diabetes.Glucose,Diabetes.Age,hue=Diabetes.Outcome,size=Diabetes.DiabetesPedigreeFunction)
Out[677]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c2608ec88>
In [ ]:

1) Majority of concentration of women are in section of age less than 32 and glucose level between 80 to 130 but probability of women having diabeties in this section is very ocassionally.

2) Almost all women of all age have diabeties whose glucose levels are 150 and above

3) Glucose is very important attribute to classify women diabetic or not

4) Majority of women having high insulin value ie 100 and above, have glucose level 150 and above and falls under age of 45

Blood Pressure and Skin Thickness

In [684]:
sns.scatterplot(Diabetes.BloodPressure,Diabetes.SkinThickness)
Out[684]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c26628e48>
In [685]:
sns.scatterplot(Diabetes.BloodPressure,Diabetes.SkinThickness,hue=Diabetes.Outcome)
Out[685]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c26a2c828>

1) Blood Pressure and Skin Thickness doesnt seems to have any relation

Blood Pressure and Insulin

In [686]:
sns.scatterplot(Diabetes.BloodPressure,Diabetes.Insulin)
Out[686]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c26a16f60>
In [687]:
sns.scatterplot(Diabetes.BloodPressure,Diabetes.Insulin,hue=Diabetes.Outcome)
Out[687]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c26c1f278>

1) Blood Pressure and Insulin doesnt seems to have any relation

BP & DPF

In [688]:
sns.scatterplot(Diabetes.BloodPressure,Diabetes.DiabetesPedigreeFunction)
Out[688]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c26c1fe80>
In [689]:
sns.scatterplot(Diabetes.BloodPressure,Diabetes.DiabetesPedigreeFunction,hue=Diabetes.Outcome)
Out[689]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c26e60278>

No Relation Between Blood Pressure & DPF

BP Vs Age

In [691]:
sns.scatterplot(Diabetes.BloodPressure,Diabetes.Age,hue=Diabetes.Outcome)
Out[691]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c2706ebe0>
In [ ]:

Skin Thickness And BMI

In [692]:
sns.scatterplot(Diabetes.SkinThickness,Diabetes.BMI)
Out[692]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c27066128>
In [693]:
sns.scatterplot(Diabetes.SkinThickness,Diabetes.BMI,hue=Diabetes.Outcome)
Out[693]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c2727c0f0>
In [694]:
sns.scatterplot(Diabetes.SkinThickness,Diabetes.BMI,hue=Diabetes.Outcome,size=Diabetes.Glucose)
Out[694]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c273a3828>
In [695]:
sns.scatterplot(Diabetes.SkinThickness,Diabetes.BMI,hue=Diabetes.Outcome,size=Diabetes.Insulin)
Out[695]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c273a39e8>

1) Skin Thickness and BMI have linear relationship where a unit change in skin thickness changes the BMI of women linearly

2) Majority of diabetic women are concentrated in section where skin thickness is 22 and above and BMI 27 and above

3) Women who are diabetic, have BMI 27 or above and skin thickness above or 22 have higher glucose levels i.e 100 and above and also higher insulin levels 100 and above

Insulin and BMI

In [701]:
sns.scatterplot(Diabetes.Insulin,Diabetes.BMI)
Out[701]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c27b6fc88>
In [702]:
sns.lmplot(x='Insulin',y='BMI',data=Diabetes)

/Users/himanshu/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
Out[702]:
<seaborn.axisgrid.FacetGrid at 0x1c27cf90b8>
In [703]:
sns.scatterplot(Diabetes.Insulin,Diabetes.BMI,hue=Diabetes.Outcome)
Out[703]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c27c92a20>

1) Insulin and BMI has some sort of linear relationship but not perfect relation

Correlation Plot

In [60]:
Diab=Diabetes.iloc[:,1:8]
In [61]:
sns.heatmap(Diab.corr(),annot=True)
Out[61]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a165960b8>

Few pairs of variables have good significat correlation of all :

1) SkinThickness and BMI

2) Insulin and Glucose

3) BMI and Insulin

4) BMI & BP

5) Age & Glucose

4) BP & Age

The relation between the two variables are both scientifically and statistically proven

In [715]:
sns.pairplot(Diab)
Out[715]:
<seaborn.axisgrid.PairGrid at 0x1c2850b9e8>

Hence this pair plot justify and summarises the above exploratory and relationship finding methedology. Now we know which variables are having relationship and can be significant in classifying women diabetic or non diabetic . Also using the above findings we can use them to make some sort of rules or associations which will classify women as diabetic or non diabetic.

It was a study on a skewed data hence there must be some affect of bias of any nature

In [ ]:
In [709]:
Diabetes.dtypes
Out[709]:
Pregnancies                 float64
Glucose                     float64
BloodPressure               float64
SkinThickness               float64
Insulin                     float64
BMI                         float64
DiabetesPedigreeFunction    float64
Age                         float64
Outcome                       int64
dtype: object
In [721]:
Diab.head()
Out[721]:
Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age
0 148.0 72.0 35.0 30.5 33.6 0.627 50.0
1 85.0 66.0 29.0 30.5 26.6 0.351 31.0
2 183.0 64.0 23.0 30.5 23.3 0.672 32.0
3 89.0 66.0 23.0 94.0 28.1 0.167 21.0
4 137.0 40.0 35.0 168.0 43.1 1.200 33.0
In [731]:
Diab['Thickness_Per_G']=Diab.SkinThickness/Diab.Glucose

Since SkinThickness and BMI are correlated , which is because skin thickness is indirectly a part of BMI as skin thickness states the thickness of the skin which is basicaly a mass index , and thickness of skin is due to obesity as one of the reason, hence obessed people must have high Glucose or sugar level, hence a new variable is being derived that is skinthickness per glucose level.

Earlier Glucose and BMI were not directly correlated but now Thickness_Per_G and BMI have a good correlation , which you can see below in the heat map.

Theoretically as BMI increases , insulin resistance also increases which results in increased blood glucose level.

In [723]:
sns.heatmap(Diab.corr(),annot=True)
Out[723]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c2a2bb0b8>
In [735]:
sns.scatterplot(Diab.BMI,Diab.Thickness_Per_G,hue=Diabetes.Outcome, size=Diab.Insulin)
Out[735]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c2b902c18>

As BMI increases , Thickness per glucose level also increases

Majority diabetic women have BMI 30 or above and Thickness per Glucose level is .4 or below

It is very rare that person with thickness per glucose be higher than .6 and still be diabetic as there glucose level will be less and so does insulin level

In [737]:
sns.scatterplot(Diab.Insulin,Diab.Thickness_Per_G)
Out[737]:
<matplotlib.axes._subplots.AxesSubplot at 0x1c2b6dac88>
In [ ]: